Biology subjects

Pierpaoli, C.

Publications and source records attributed to Pierpaoli, C..

5 recordsLinked to original sources

bioRxiv2024

Exploiting Four-way Phase-encoding Benefits for Robust Detection and Correction of EPI artifacts: Application to Residual Ghosts in Diffusion MRI.

PurposeTo propose and develop an image processing-based methodology for detecting and correcting echo planar imaging (EPI) artifact that employs data from 4-way phase-encoding acquisitions. ApproachPrevious studies have demonstrated that acquiring images with four different phase-encoding directions can improve the reproducibility of diffusion derived quantitative maps while retaining the same acquisition time. This improvement is achieved by averaging across phase-encoding directions (PEDs) to reduce the impact of EPI artifacts. Building on this principle, the proposed method further improves this 4-way encoding approach by leveraging the properties of signal distributions to detect artifactual regions. Additionally, the method can be tailored for specific artifact manifestations by considering their localization due to underlying acquisition parameters. The proposed method is applied to and validated using both simulated data and in-vivo diffusion MRI data affected by residual ghost artifacts. ResultsSimulations with known ground-truth images demonstrated high artifact detection accuracy, achieving a Dice score of 0.91 for reconstructions without parallel imaging. In the in-vivo dataset, the method also improved longitudinal reproducibility, reducing variability by 30% in ghost-affected regions. ConclusionThe proposed correction method effectively detected and corrected residual ghost artifacts without the need of any additional k-space data. This retrospective approach can be directly integrated into existing processing pipelines to further improve the quality of EPI images and enhancing image quality in studies that utilize 4-way PEDs acquisition.

bioengineering↗

bioRxiv2023

'Pscore' - A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics

BACKGROUNDQuantitative MRI metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed. PURPOSETo confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, Pscore to address this issue. STUDY TYPERetrospective cohort POPULATION961 healthy young-adults (age:22-35 years, Females:53%) from the Human Connectome Project FIELD STRENGTH/SEQUENCE3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE ASSESSMENTThe dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU-atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate Pscores- which normalized the difference between the median and a participants value with the corresponding difference between the median and the 5th/95th percentile values. STATISTICAL TESTSROI-wise distributions were assessed using Log transformations, Zscore, and the Pscore methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95(%),PEV<5(%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (n=100) using 100 iterations. RESULTSThe dMRI metric distributions were systematically non-Gaussian, including positively skewed (e.g., mean and radial diffusivity) and negatively skewed (e.g., fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95=5%,PEV<5=5%) whereas Pscore distributions were symmetric and balanced (PEV>95=PEV<5=5%); even for small bootstrapped samples (average [Formula]. DATA CONCLUSIONThe inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed Pscore method may help estimating individual deviations more accurately in skewed normative data, even from small datasets.

neuroscience↗

bioRxiv2020

Practical gradient non-linearity correction of multi-site diffusion weighted MRI with empirical field maps

BackgroundAchieving inter-site / inter-scanner reproducibility of diffusion weighted magnetic resonance imaging (DW-MRI) metrics has been challenging given differences in acquisition protocols, analysis models, and hardware factors. PurposeMagnetic field gradients impart scanner-dependent spatial variations in the applied diffusion weighting that can be corrected if the gradient nonlinearities are known. However, retrieving manufacturer nonlinearity specifications is not well supported and may introduce errors in interpretation of units or coordinate systems. We propose an empirical approach to mapping the gradient nonlinearities with sequences that are supported across the major scanner vendors. Study TypeProspective observational study SubjectsA spherical isotropic diffusion phantom, and a single human control volunteer Field Strength/Sequence3T (two scanners). Stejskal-Tanner spin echo sequence with b-values of 1000, 2000 s/mm2 with 12, 32, and 384 diffusion gradient directions per shell. AssessmentWe compare the proposed correction with the prior approach using manufacturer specifications against typical diffusion pre-processing pipelines (i.e., ignoring spatial gradient nonlinearities). In phantom data, we evaluate metrics against the ground truth. In human and phantom data, we evaluate reproducibility across scans, sessions, and hardware. Statistical TestsWilcoxon rank-sum test between uncorrected and corrected data. ResultsIn phantom data, our correction method reduces variation in mean diffusivity across sessions over uncorrected data (p<0.05). In human data, we show that this method can also reduce variation in mean diffusivity across scanners (p<0.05). ConclusionOur method is relatively simple, fast, and can be applied retroactively. We advocate incorporating voxel-specific b-value and b-vector maps should be incorporated in DW-MRI harmonization preprocessing pipelines to improve quantitative accuracy of measured diffusion parameters.

neuroscience↗

bioRxiv2020

Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging

We describe a practical two-dimensional (2D) diffusion MRI framework to deliver specificity and improve sensitivity to axonal injury in the spinal cord. This approach provides intravoxel distributions of correlations of water mobilities in orthogonal directions, revealing sub-voxel diffusion components. Here we use it to investigate water diffusivities along axial and radial orientations within spinal cord specimens with confirmed, tract-specific axonal injury. First, we show using transmission electron microscopy and immunohistochemistry that tract-specific axonal beading occurs following Wallerian degeneration in the cortico-spinal tract as direct sequelae to closed head injury. We demonstrate that although some voxel-averaged diffusion tensor imaging (DTI) metrics are sensitive to this axonal injury, they are non-specific, i.e., they do not reveal an underlying biophysical mechanism of injury. Then we employ 2D diffusion correlation imaging (DCI) to improve discrimination of different water microenvironments by measuring and mapping the joint water mobility distributions perpendicular and parallel to the spinal cord axis. We determine six distinct diffusion spectral components that differ according to their microscopic anisotropy and mobility. We show that at the injury site a highly anisotropic diffusion component completely disappears and instead becomes more isotropic. Based on these findings, an injury-specific MR image of the spinal cord was generated, and a radiological-pathological correlation with histological silver staining % area was performed. The resulting strong and significant correlation (r = 0.70, p < 0.0001) indicates the high specificity with which DCI detects injury-induced tissue alterations. We predict that the ability to selectively image microstructural changes following axonal injury in the spinal cord can be useful in clinical and research applications by enabling specific detection and increased sensitivity to injury-induced microstructural alterations. These results also encourage us to translate DCI to higher spatial dimensions to enable assessment of traumatic axonal injury, and possibly other diseases and disorders in the brain.

neuroscience↗

bioRxiv2018

Limits to anatomical accuracy of diffusion tractography using modern approaches

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of thebrain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the ISBI 2018 3D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractographys inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

neuroscience↗